// Do one forward pass of priorBox layer and check to see if its output
// matches the given result
void doOnePriorBoxTest(size_t feature_map_width,
size_t feature_map_height,
size_t image_width,
size_t image_height,
vector<int> min_size,
vector<int> max_size,
vector<real> aspect_ratio,
vector<real> variance,
bool use_gpu,
MatrixPtr& result) {
// Setting up the priorbox layer
TestConfig configt;
configt.layerConfig.set_type("priorbox");
configt.inputDefs.push_back({INPUT_DATA, "featureMap", 1, 0});
LayerInputConfig* input = configt.layerConfig.add_inputs();
configt.inputDefs.push_back({INPUT_DATA, "image", 1, 0});
configt.layerConfig.add_inputs();
PriorBoxConfig* pb = input->mutable_priorbox_conf();
for (size_t i = 0; i < min_size.size(); i++) pb->add_min_size(min_size[i]);
for (size_t i = 0; i < max_size.size(); i++) pb->add_max_size(max_size[i]);
for (size_t i = 0; i < variance.size(); i++) pb->add_variance(variance[i]);
for (size_t i = 0; i < aspect_ratio.size(); i++)
pb->add_aspect_ratio(aspect_ratio[i]);
// data layer initialize
std::vector<DataLayerPtr> dataLayers;
LayerMap layerMap;
vector<Argument> datas;
initDataLayer(
configt, &dataLayers, &datas, &layerMap, "priorbox", 1, false, use_gpu);
dataLayers[0]->getOutput().setFrameHeight(feature_map_height);
dataLayers[0]->getOutput().setFrameWidth(feature_map_width);
dataLayers[1]->getOutput().setFrameHeight(image_height);
dataLayers[1]->getOutput().setFrameWidth(image_width);
// test layer initialize
std::vector<ParameterPtr> parameters;
LayerPtr priorboxLayer;
initTestLayer(configt, &layerMap, ¶meters, &priorboxLayer);
priorboxLayer->forward(PASS_GC);
checkMatrixEqual(priorboxLayer->getOutputValue(), result);
}
static void getMKLDNNConvConfig(TestConfig& cfg, const testConvDesc& pm) {
cfg.layerConfig.set_type("mkldnn_conv");
cfg.layerConfig.set_active_type("relu");
cfg.layerConfig.set_num_filters(pm.oc);
cfg.layerConfig.set_size(pm.oc * pm.oh * pm.ow);
cfg.layerConfig.set_shared_biases(true);
cfg.inputDefs.push_back(
{INPUT_DATA,
"layer_0",
/* size of input layer= */ size_t(pm.ic * pm.ih * pm.iw),
/* size of weight= */ size_t(pm.oc * pm.ic * pm.fh * pm.fw / pm.gp)});
LayerInputConfig* input = cfg.layerConfig.add_inputs();
ConvConfig* conv = input->mutable_conv_conf();
conv->set_groups(pm.gp);
conv->set_img_size(pm.iw);
conv->set_img_size_y(pm.ih);
conv->set_output_x(pm.ow);
conv->set_output_y(pm.oh);
conv->set_filter_size(pm.fw);
conv->set_filter_size_y(pm.fh);
conv->set_channels(pm.ic);
conv->set_padding(pm.pw);
conv->set_padding_y(pm.ph);
conv->set_stride(pm.sw);
conv->set_stride_y(pm.sh);
conv->set_dilation(pm.dw);
conv->set_dilation_y(pm.dh);
conv->set_caffe_mode(true);
conv->set_filter_channels(conv->channels() / conv->groups());
CHECK_EQ(conv->filter_channels() * pm.gp, conv->channels())
<< "it is indivisible";
int fh = (pm.fh - 1) * pm.dh + 1;
int fw = (pm.fw - 1) * pm.dw + 1;
int ow = outputSize(pm.iw, fw, pm.pw, pm.sw, true);
int oh = outputSize(pm.ih, fh, pm.ph, pm.sh, true);
CHECK_EQ(ow, pm.ow) << "output size check failed";
CHECK_EQ(oh, pm.oh) << "output size check failed";
}
static void getMKLDNNBatchNormConfig(TestConfig& cfg,
const testBatchNormDesc& pm) {
cfg.layerConfig.set_size(pm.ic * pm.ih * pm.iw);
cfg.layerConfig.set_type("mkldnn_batch_norm");
cfg.biasSize = pm.ic;
cfg.inputDefs.push_back(
{INPUT_DATA,
"layer_0",
/* size of input layer= */ size_t(pm.ic * pm.ih * pm.iw),
/* size of weight= */ size_t(pm.ic)});
cfg.inputDefs.push_back(
{INPUT_DATA, "layer_1_moving_mean", 1, size_t(pm.ic)});
cfg.inputDefs.back().isStatic = true;
cfg.inputDefs.push_back({INPUT_DATA, "layer_2_moving_var", 1, size_t(pm.ic)});
cfg.inputDefs.back().isStatic = true;
LayerInputConfig* input = cfg.layerConfig.add_inputs();
cfg.layerConfig.set_active_type("relu");
cfg.layerConfig.add_inputs();
cfg.layerConfig.add_inputs();
ImageConfig* img_conf = input->mutable_image_conf();
img_conf->set_channels(pm.ic);
img_conf->set_img_size_y(pm.ih);
img_conf->set_img_size(pm.iw);
}
// Test that the convTrans forward is the same as conv backward
TEST(Layer, convTransLayerFwd) {
// Setting up conv-trans layer
TestConfig configt;
configt.biasSize = 3;
configt.layerConfig.set_type("exconvt");
configt.layerConfig.set_num_filters(3);
configt.layerConfig.set_partial_sum(1);
configt.layerConfig.set_shared_biases(true);
configt.inputDefs.push_back({INPUT_DATA, "layer_0", 1024, 384});
LayerInputConfig* input = configt.layerConfig.add_inputs();
ConvConfig* conv = input->mutable_conv_conf();
conv->set_filter_size(2);
conv->set_filter_size_y(4);
conv->set_channels(16);
conv->set_padding(0);
conv->set_padding_y(1);
conv->set_stride(2);
conv->set_stride_y(2);
conv->set_groups(1);
conv->set_filter_channels(3 / conv->groups());
conv->set_img_size(16);
conv->set_output_x(outputSize(conv->img_size(), conv->filter_size(),
conv->padding(), conv->stride(),
/* caffeMode */ true));
configt.layerConfig.set_size(conv->img_size() * conv->img_size() *
configt.layerConfig.num_filters());
configt.layerConfig.set_name("convTrans");
// data layer initialize
std::vector<DataLayerPtr> dataLayers;
LayerMap layerMap;
vector<Argument> datas;
initDataLayer(configt, &dataLayers, &datas, &layerMap, "convTrans",
100, false, false);
// test layer initialize
std::vector<ParameterPtr> parameters;
LayerPtr convtLayer;
initTestLayer(configt, &layerMap, ¶meters, &convtLayer);
convtLayer->getBiasParameter()->zeroMem();
convtLayer->forward(PASS_GC);
// Setting up conv-layer config
TestConfig config;
config.biasSize = 16;
config.layerConfig.set_type("exconv");
config.layerConfig.set_num_filters(16);
config.layerConfig.set_partial_sum(1);
config.layerConfig.set_shared_biases(true);
config.inputDefs.push_back({INPUT_DATA, "layer_1", 768, 384});
input = config.layerConfig.add_inputs();
conv = input->mutable_conv_conf();
conv->set_filter_size(2);
conv->set_filter_size_y(4);
conv->set_channels(3);
conv->set_padding(0);
conv->set_padding_y(1);
conv->set_stride(2);
conv->set_stride_y(2);
conv->set_groups(1);
conv->set_filter_channels(conv->channels() / conv->groups());
conv->set_img_size(16);
conv->set_output_x(outputSize(conv->img_size(), conv->filter_size(),
conv->padding(), conv->stride(),
/* caffeMode */ true));
config.layerConfig.set_size(conv->output_x() * conv->output_x() *
config.layerConfig.num_filters());
config.layerConfig.set_name("conv");
// data layer initialize
std::vector<DataLayerPtr> dataLayers2;
LayerMap layerMap2;
vector<Argument> datas2;
initDataLayer(config, &dataLayers2, &datas2, &layerMap2, "conv",
100, false, false);
// test layer initialize
std::vector<ParameterPtr> parameters2;
LayerPtr convLayer;
initTestLayer(config, &layerMap2, ¶meters2, &convLayer);
// Sync convLayer and convtLayer parameter
convLayer->getBiasParameter()->zeroMem();
convLayer->getParameters()[0]->getBuf(PARAMETER_VALUE)->copyFrom(
*(convtLayer->getParameters()[0]->getBuf(PARAMETER_VALUE)));
// Set convLayer outputGrad as convTransLayer input value
convLayer->forward(PASS_GC);
convLayer->getOutput().grad->copyFrom(*(dataLayers[0]->getOutputValue()));
vector<int> callbackFlags(parameters2.size(), 0);
auto callback = [&](Parameter* para) { ++callbackFlags[para->getID()]; };
convLayer->backward(callback);
// Check that the convLayer backward is the same as convTransLayer forward
checkMatrixEqual(convtLayer->getOutputValue(),
dataLayers2[0]->getOutputGrad());
}
